Papers with multi-task active learning

1 papers
Multi-task Active Learning for Pre-trained Transformer-based Models (2022.tacl-1)

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Challenge: Multi-task learning requires annotating the same text with multiple annotation schemes, which can be costly and laborious.
Approach: They propose to use multi-task active learning paradigm to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model.
Outcome: The proposed model minimizes annotation efforts for multi-task NLP models by iterating on the most valuable examples.

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